Quantifying the risk of Amazon forest 'dieback' from climate and land-use change Ben Poulter Swiss Federal Research Institute WSL in collaboration with

Quantifying the risk of Amazon forest 'dieback' from climate and

land-use change

Ben Poulter

Swiss Federal Research Institute WSLin collaboration with the Marie Curie Greencycles RTN and the

Potsdam Institute for Climate Impact Research (PIK)

June 7/8 2010 LSCE / CEA 2

Outline

• Drivers of Amazon forest dieback• Understanding of Amazon forest ecology• Modeling uncertainty of tropical forest

dynamics• Modeling drivers and their synergies• Managing uncertainty


i. drivers of Amazon forest dieback

Cox et al. 2004

1.Climate change1. Reduced precipitation & increasing

temperature2. Dieback of forest & enhanced reduction

in precip. via convective precipitation3. Replicated with perturbed physics

ensemble4. Agreement between models

1. Spatio-temporal variability2. Climate scenario dependent

Booth et al. in rev.

Cox et al. 2004

Sitch et al. 2008

Salazar et al. 2007

Unresolved:What are climate and ecological mechanisms driving forest dieback?What is likelihood of climate driven forest dieback?


i. drivers of Amazon forest dieback1. ClimateWhat are climatic & ecological mechanisms driving forest dieback?What is likelihood of climate driven forest dieback?

1.Deforestation1. Arc of deforestation2. Future deforestation linked to

connectedness and access3. Estimating C-emissions is

challenging

Loarie et al. 2009Soares et al. 2006

Unresolved:Spatial pattern is predictableIntensity of deforestation linked global economic teleconnectionsTracking fate of carbon remains challenging

Rammankutty et al. 2007


Morton et al. 2008

i. drivers of Amazon forest dieback1. ClimateWhat are climatic & ecological mechanisms driving forest dieback?What is likelihood of climate driven forest dieback?

2. DeforestationSpatial pattern is predictableIntensity of deforestation linked global economic teleconnectionsTracking fate of carbon remains challenging

1.Fire1. Deforestation related

1. human ignitions2. micro-climate

2. Climate amplifies3. ~100% biomass consumption

Aragao et al. 2007

Morton et al. 2008


i. drivers of Amazon forest dieback

Nepstad 2008

SynergiesHow will interactions affect spatio-temporal dynamics of Amazon forest dieback?Is there information in the spatial temporal pattern of uncertainties useful for biodiveristy protection, REDD, etc.?

1. ClimateWhat are climatic & ecological mechanisms driving forest dieback?What is likelihood of climate driven forest dieback?

2. DeforestationSpatial pattern is predictableIntensity of deforestation linked global economic teleconnectionsTracking fate of carbon remains challenging

3. FireLinked to climate and deforestationStrong feedback on forest degradation


Outline


dynamics• Modeling drivers and their synergies• Is reducing uncertainty possible?


IPCC AR4 2007

ii. understanding of Amazon forest ecology

Li et al. 2006

1.Climate1. GCM model disagreement2. Model-obs. disagreement

Malhi et al. 2009


Phillips et al. 1998


1.Aboveground processes1. Biomass

1. Increasing1.Radiation (Hashimoto et al. 2009)

2.CO23.Disturbance (Gloor et al. 2010)

2. Sensitivity to drought

2. Canopy processes1. Dynamic phenology

1.Sustained by deep soils2. Resilient to drought3. Not resilient to drought

Phillips et al. 2009Myneni et al. 2007

June 7/8 2010 LSCE / CEA 10Poulter and Cramer, 2009


Experiment 1

• Tested robustness of seasonal cycle to increasing data quality

(BISE filter, QA/QC filters)

• EVI and LAI seasonality sensitive to atmospheric contamination



Proposed mechanisms sustaining seasonal forest dynamics:- Deep soils and roots (18 m; Nepstad et al. 1994) Maintain GPP during dry season (Saleska et al. 2003) - Green up is an anticipatory response to light (Myneni et al. 2007) Wet tropical forests are radiation limited (Nemani et al. 2004)

Saleska et a. 2007

Saleska et al. 2003

Ecosystem models get seasonal cycle wrong


Experiment 2• Tested relative effects of:

– deep soils / roots and,

– dynamic 'anticipatory' tropical phenology

– Using the LPJ DGVM

– Dry season length gradient


Stockli et al. 2008

Poulter et al. 2009

Poulter et al. 2009


Tra

nsf

orm

ed b

y p

roce

ss m

od

ule

s in

toClimate, Soil, CO2

C budget, H2O Budget,Vegetation Composition

10 plant functional types

competition, mortality, establishment

fire (globfirm)

photosynthesis: coupled C and H2O cycles

C allocation (funct. and struct. relations)

Carbon pools: 4 in vegetation, 4 in litter/soil

Full hydrology

AET

Ci

AET

Ci

crown area

height

fine roots

leaves

LAI

sapwoodheartwood

0-50 cm50-150 cm

stemdiameter

Spa

ce &

T

ime

Loop

s

LPJml Dynamic Vegetation Model



• Deep soils required to maintain dry season GPP

• Dynamic LAI not required (fpar saturation, dynamic Vcmax)

modis gpp = grey trianglesshallow soil = black triangles/squaresdeep soil = black diamonds/circlesdynamic phen = black circles/squares

Leaf Area Index (LAI)

Low HighLow

High

Fra

ctio

n o

f P

ho

tosy

nth

etic

A

vaila

ble

Rad

iati

on

(F

PA

R)

X%

X%

Poulter et al. 2009


Outline




iii. modeling uncertainty of tropical forest dynamics

Experiment 3• Identify sources of uncertainty for

projecting climate impacts in Amazon Basin

– Identify key parameters and their spatial influence

– Partition uncertainty between vegetation model and climate projection

• Methods– LPJml DGVM– Latin Hypercube Analysis– Ensemble of GCM models (8)– SRES A2 storyline– Variance partitioning following Hawkins

et al. 2009

Latin hypercube

Random sampleSet included 42 parameters and evaluated against eddy flux data (1000 sets).

For example:Soil depthRooting distributionRespiration Q10Maximum transpirationMinimum conductance…

20 parameters identified as important for determining variability of key outputs and used for basinwide runs (400 sets)

Soil depthRooting distributionRespiration Q10Maximum transpirationMinimum conductance…

Poulter et al. 2010



Experiment 3• GCM model selection provided

range of precipitation (+/-) and temperature projections (+/++)

• Benchmarking– Compared to flux towers and

biomass data– Parameter sets resulting in

unrealistic outcomes removed– Site comparison did not

include local effects (floodplain, management history)



Change in aboveground C-stocks -16 to +30 Pg C change

Change in forest cover -13 to +2% increase

Parameters-Initial PFT composition influential

- via competitive parameters (TO, alpha)

- Establishment - recovery- Soil depth - water access- Rooting depth:

- >> roots in upper layer less water access



Combining parameter uncertainty with GCM uncertainty:- Climate projection main source of uncertainty

Variance partitioning- IV important ~10-20 yrs- Spatial variability in importance of GCM uncertainty- Signal to noise ratio < 1 in E. Amazonia, greater than 1 in W. Amazonia until ~2060

East Amazonia West Amazonia


Outline




iv. modeling drivers and their synergies

Experiment 4• Coupled land-use dynamics with LPJml

– New deforestation-fire function added to GlobFirm– NOAA-12 hot pixels– Scalar modifies area burnt-fire season length– As deforestation increases, longer fire season length…

• Ensembles/factorial approach– 9 GCM models (SRES A2)

• (no climate feedback)– 2 deforestation scenarios (based on Soares et al. 2005)

• 40% reduction by 2050• Interpolated to 2100 assuming today's conservation areas


iv. modeling drivers and their synergies

Current NBP-0.49 to -0.12 PgC a-1

Future NBP (2100)-0.40 to 0.97 PgC a-1

Change in carbon stocks - Climate change / CO2 : -16 to +33 PgC + fire : -19 to +33 PgC + deforestation : -40 to + 12 PgC -

Previous studies- Soares - 32 PgC loss from deforestation- Cox - 35 PgC loss from climate change-

Low agreement between climate projections: - 37% agreement in sign of NBP change in 2100

Linear climate response, with increasing importance of synergies with more extreme climate change


Outline




v. Managing uncertainty

• “…Where there are threats of serious or irreversible damage, lack of full scientific certainty should not be used as a reason for postponing such measures” UNFCCC 1992

• Risk management of tropics– Spatio-temporal dimensions

• Model developments– Canopy dynamics– Acclimation

• Photosynthesis• Respiration

– PFT diversity– Hydrology

• Hydraulic lift• Deep soils/roots

– Climate Cox and Stephenson 2007

imp

ort

an

ce


• Questions?– Email: [email protected]

• Papers…– Poulter B, Aragao L, Heinke J, et al. (2010a) Net biome production of the Amazon Basin in the 21st Century. Global

Change Biology, doi: 10.1111/j.1365-2486.2009.02064.x.– Poulter B, Cramer W (2009a) Satellite remote sensing of tropical forest canopies and their seasonal dynamics.

International Journal of Remote Sensing, 30, 6575-6590.– Poulter B, Hattermann F, Hawkins E, et al. (2010b) Robust dynamics of Amazon dieback to climate change with perturbed

ecosystem model parameters. Global Change Biology, doi: 10.1111/j.1365-2486.2009.02157.x.– Poulter B, Heyder U, Cramer W (2009b) Modelling the sensitivity of the seasonal cycle of GPP to dynamic LAI and soil

depths in tropical rainforests. Ecosystems, 12, 517-533.

• Acknowledgements– Wolfgang Cramer, Andrew Friend, Ursula Heyder, Fred Hatterman, Soenke Zaehle, Ed Hawkins, Stephen Sitch,

Greencycles RTN

Documents

Quantifying the risk of Amazon forest 'dieback' from climate and land-use change Ben Poulter Swiss Federal Research Institute WSL in collaboration with